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Expert Opinion on Drug Discovery Volume 17, 2022 - Issue 1
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Review

Guided structure-based ligand identification and design via artificial intelligence modeling

Juan I. Di Filippoa Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina;b Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina;c Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, ArgentinaORCID Iconhttps://orcid.org/0000-0002-3118-5314View further author information
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Claudio N. Cavasottoa Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina;b Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina;c Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, ArgentinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1372-0379View further author information
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Pages 71-78 | Received 14 Jul 2021, Accepted 08 Sep 2021, Published online: 23 Sep 2021

Citations (3)

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Read on this site (1)

Felix Potlitz, Andreas Link & Lukas Schulig. (2023) Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opinion on Drug Discovery 18:3, pages 303-313.
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Articles from other publishers (2)

Claudio N. Cavasotto & Juan I. Di Filippo. (2023) The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking. Journal of Chemical Information and Modeling.
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Lina Dong, Xiaoyang Qu & Binju Wang. (2022) XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking. ACS Omega 7:25, pages 21727-21735.
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